mvMISE: A General Framework of Multivariate Mixed-Effects Selection Models

Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness.

Version: 1.0
Depends: lme4, MASS
Published: 2018-06-10
DOI: 10.32614/CRAN.package.mvMISE
Author: Jiebiao Wang and Lin S. Chen
Maintainer: Jiebiao Wang < at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: mvMISE results


Reference manual: mvMISE.pdf


Package source: mvMISE_1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvMISE_1.0.tgz, r-oldrel (arm64): mvMISE_1.0.tgz, r-release (x86_64): mvMISE_1.0.tgz, r-oldrel (x86_64): mvMISE_1.0.tgz


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